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- Outliers treatment to improve the recognition of voice pathologiesPublication . Silva, Letícia; Hermsdorf, Juliana; Guedes, Victor; Teixeira, Felipe; Fernandes, Joana Filipa Teixeira; Bispo, Bruno; Teixeira, João PauloIn some of the processes used in data analysis, such as the recognition of pathologies and pathological subjects, the presence of anomalous instances in the dataset is an unfavorable situation that can lead to misleading results. This article presents a function that implements the identification of anomalies in dataset using the boxplot and standard deviation methods. Also was used the filling technique to treat these anomalies, in which the anomalous point value were substituted by a limit value determined by the boxplot or standard deviation methods. To improve the outliers methods some normalization processes based on the z-score, logarithmic and squared root methodologies were experimented. These outliers treatment were applied to the dataset used in the recognition of vocal pathologies (dysphonia, chronic laryngitis and vocal cords paralysis vs control), performed by a MLP and LSTM neural networks. After the experiments, both the standard deviation and the boxplot methods with z-score normalization showed very useful for pre-processing the dataset for voice pathologies recognition. The accuracy was improved between 3 and 13 points in percentage.
- Long short term memory on chronic laryngitis classificationPublication . Guedes, Victor; Candido Junior, Arnaldo; Fernandes, Joana Filipa Teixeira; Teixeira, Felipe; Teixeira, João PauloThe classification study with the use of machine learning concepts has been applied for years, and one of the aspects in which this can be applied is for the analysis of speech acoustics applied to the analysis of pathologies. Among the pathologies present, one of them is chronic laryngitis. Thus, this article aims to present the results for a classification of chronic laryngitis with the use of Long Short Term Memory as a classifier. The parameters of relative jitter, relative shimmer and autocorrelation was used as input of the LSTM. A dataset of about 1500 instances were used to train, validate and test along 4 experiments with LSTM and one feedforward Artificial Neural Network (ANN). The results of the LSTM overcome the ones of the feedforward ANN, and was about 100% accuracy, sensitivity and specificity in test set, denoting a promising future for this classification tool in the voice pathologies diagnose.